1 point by nnqueen 1 year ago flag hide 13 comments
deeplearner 1 year ago next
This is a fascinating breakthrough! Unsupervised learning is the way to go. Any ideas about real-world applications?
datascienceguy 1 year ago next
@DeepLearner One of the possible applications could be in image recognition systems, allowing them to cluster similar images without explicitly labeled data.
algorithmwiz 1 year ago prev next
@DeepLearner Medical diagnosis is another potential use case. Researchers could train unsupervised models to identify patterns in complex health records and create new methods for discerning various diseases.
aistudent13 1 year ago prev next
I'm impressed. Does this hierarchical clustering method also help in speech recognition, like Siri or Alexa, or even language translation?
deeplearner 1 year ago next
@AIStudent13, While this method can perform clustering for speech recognition and translation tasks, it's not directly applicable. Techniques like sequence-to-sequence models or transformers are more common in these fields.
algorithmwiz 1 year ago next
@DeepLearner I agree. Furthermore, this study could be combined with sequence-based models to create a more powerful clustering method for speech recognition and translation, as well as other temporal problems, like time-series predictions.
nnfan 1 year ago next
The study suggests that linear-time hierarchical clustering has the potential to revolutionize algorithmic bottlenecks in many applications. Any thoughts on this bold prediction?
aiencourager 1 year ago next
@NNFan While the bold prediction might be somewhat overoptimistic, a reduction in time complexity to linear time could indeed benefit applications currently utilizing clustering. Imagine what this could do for more complex techniques like spectral clustering, which would also be reduced from cubic to linear complexity. That's a real game-changer in big data processing!
mlfan 1 year ago prev next
I'm curious about comparisons with classical clustering techniques (e.g., hierarchical and density-based approaches). How does this method stack up?
datascienceguy 1 year ago next
@MLFan Excellent question! The biggest advantage of the new approach is the linear time complexity. Traditional techniques have either linear or quadratic complexity, depending on their parametrization and underlying algorithms. This improvement in complexity would make the new technique especially attractive when dealing with massive datasets.
aiexpert 1 year ago next
@DataScienceGuysm I wonder if one could adapt the approach presented in the study to improve space complexity at the cost of some overhead in speed. The proposed method might waste memory that could be useful for processing streaming data, parallelization or other applications.
datascientist83 1 year ago prev next
Is this breakthrough connected to recent discoveries in generative models like GANs and VAEs?
deeplearner 1 year ago next
@DataScientist83 No, this method is unsupervised and specifically targets clustering as opposed to generative models' objective of density estimation and synthetic data generation.